Design of a Web-Based Expert System Using Certainty Factors for Internal Disease Diagnosis
Abstract
Diseases are classified into different types based on the symptoms, causes, and disturbances they cause to the human body. Among the disease categories that have a wide range of sub-specialties and branches is internal medicine. Early disease detection is difficult because many diseases exhibit similar symptoms. To diagnose the disease, medical specialists will conduct a series of physical and laboratory tests. However, before going to the doctor, the patient should learn about the disease and begin treatment so that it does not worsen and become dangerous. As a result, we require a system that can assist patients in receiving information and detecting their disease early. An expert system was developed in this study to help people obtain complete and precise information about their disease. It is hoped that by using this system, people will be able to obtain preliminary information before visiting a doctor, thereby reducing the risk. An expert system is a type of artificial intelligence application that is a computer program designed on knowledge and rules that could act similarly to or as well as an expert. Expert systems must be able to function in the presence of uncertainty. A number of theories have been developed to deal with uncertainties, one of which is the certainty factor method, which is ideal for analyzing uncertain data. This technique provides a solution by calculating the values of certainty and uncertainty. This study concentrated on three types of internal medicine and 27 symptoms to be diagnosed; the end result was a diagnosis of the type of internal medicine encountered and the percentage of confidence, along with treatment recommendations.
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References
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